Globally, there is increased concern of the potential impacts of extreme climate events and their impact on loss and damage of people, assets and property as a result of these events. Therefore, natural partners in using climate services to assess risk are the Global Insurance Sector, who are key implementers in increasing societies resilience and recovery of extreme events and who are integral, co-design partners in this programme. This project intends to operationalize a system, called the Oasis Loss Modelling Framework, that combines climate services with damage and loss information and provides a standardised risk assessment process that can assess potential losses, areas at most risk and quantify financial losses of modelled scenarios. We intend to prove the Oasis LMF system through undertaking a range of demonstrators linked and co-designed to ‘real’ situations and end-user communities in the insurance, municipalities and business sectors (see list of partners & collaborators). These demonstrators have already been agreed with our end-users and develop work around hydro-climatic risk (in the Danube Region), Typhoon Risk, African Farmer Risk – through using climate information to support the underwriting of micro-insurance, climate v health and climate v forest asset risk assessment. We also intend to further expand access by all sectors to the models, tools and services developed within this programme and the broader climate services sector by operationalizing an open eMarket place and matchmaking facility for catastrophe and climate data and models, tools and services and through broadening awareness in the climate modelling and end-users communities to the Framework, and the transparent and comparable standard it offers to support evidence based risk assessment and adaptation planning.

Catastrophe risk models ("Cat models") are important tools used by the insurance industry to quantify risks associated with a wide variety of insurance and reinsurance products. The market for Cat models is approximately £400m globally. It is growing as the new EU regulatory framework for the insurance industry (Solvency II) requires insurance companies to display a quantitative understanding of the risks resulting from their sales of insurance products, including an understanding of the uncertainties in the Cat models that they use to assess these risks. At present almost all Cat models are commercial-in-confidence products from 3 companies. A need for more diverse Cat models and open model design is reflected in insurance industry support for the Oasis Loss Modelling Framework for open and transparent catastrophe risk modelling. Oasis is designed to combine hazard model and vulnerability model modules, built by external experts, with standard modules for inputting exposure data and carrying out financial calculations, to produce new, well-validated and Solvency-II compliant Cat models. Furthermore, recent tsunami disasters, most notably the Tohoku 2011 tsunami, have highlighted both the large potential losses to which the insurance industry is exposed in important tsunami-prone regions such as Japan and Cascadia (NW United States of America and Pacific Canada), and the lack of available scientifically sound tsunami Cat models. This application builds upon (i) our existing research on tsunami wave physics models, especially on the rigorous quantification of uncertainties in their outputs using statistical emulation methods, and (ii) an existing proof-of-concept investigation of how to produce tsunami hazard maps, compatible with the Oasis framework, from the advanced tsunami wave physics model VOLNA. We will do this by producing a working tsunami hazard model for the Cascadia region, and a simple empirical tsunami vulnerability model for common building types. These will be combined with Oasis' exposure and financial calculation modules to produce a demonstration tsunami Cat model for Cascadia in a form suitable to be used, at least for test and validation purposes, by the Oasis partner companies in the insurance industry. Our Cascadia tsunami hazard model will be the primary product of the project. Its objectives are: 1. To define, using published geological evidence, the range of possible subduction zone earthquake sources (shapes, kinematics of the ruptures) in Cascadia, and their occurrences. 2. To build a tsunami hazard model for Cascadia with runs from the tsunami model VOLNA as well as the computationally efficient statistical representation of VOLNA to cover the ranges of possible outputs that result from the range of possible earthquake sources. These VOLNA runs will be designed using state-of-the-art design of experiments methods. 3. To construct vulnerability curves for buildings that reflect published evidence derived from damage surveys after recent major tsunamis. 4. To embed these hazard and vulnerability modules into the Catastrophe modelling platform from the Oasis Loss Modelling Framework. To calculate loss exceedance probability curves for synthetic and given portfolios. 5. To propagate the uncertainties in 1-3 into the loss calculations in step 4. 6. To provide model & user documentations to enable uptake of the model by the Insurance Industry partners of Oasis.

Climate and environmental (CE) risks (CER) to our economy and society are accelerating. CER include climate-related physical risks such as floods, storms, or changing growing seasons; climate-related transition risks such as carbon pricing and climate litigation; and environmental risks such as biodiversity loss. It is now well accepted that CER can impact asset values across multiple sectors and pose a threat to the solvency of financial institutions (FIs). This can cause cascading effects with the potential to undermine financial stability. The adoption of CER analytics will ensure that CE risks can be properly measured, priced, and managed by individual FIs and across the financial system. This is also a necessary condition to ensure that capital is allocated by FIs towards technologies, infrastructure, and business models that lower CER, which are also those required to deliver the net zero carbon transition, climate resilience, and sustainable development. These twin tracks - greening finance and financing green - are both enabled by CER analytics being appropriately used by FIs. The UK is a world-leader in Green Finance (GF). UK FIs have played a key role in GF innovation. Yet, despite these advances and leadership in almost every aspect of GF, UK FIs cannot secure the data and analytics needed to properly measure and manage their exposures to CER. While the last decade has seen the exponential growth of CE data, as well as improved analytics and methods, often produced by world-leading UK science, the vast majority of this has not found its way into FI decision-making. Our vision for CERAF is to establish a new national centre to resolve this disconnect. CERAF aims to enable a step-change in the provision and accessibility of data, analytics, and guidance and accelerate the integration of CER into products and decisions by FIs to manage CER risks and drive efficient and sustainable investment decisions, thereby delivering the following impacts: - Enhance the solvency of individual FIs in the UK and globally and so contribute to the resilience of the global financial system as a whole for all, as well the efficient pricing and reallocation of capital away from assets at risk to those that are more resilient. - Underpin the development and the growth of UK GF-related products and services. - Enable a vibrant ecosystem of UK enterprises providing CER analytics and realise the opportunity for UK plc of being a world-leader in the creation and provision of CER services. Our vision is that CERAF will be the nucleus of a new national centre established to deliver world-leading research, information, and innovation to systematically accelerate the adoption and use of CER data and analytics by FIs and to unlock opportunities for the UK to lead internationally in delivering CER services to support advancements in greening finance and financing green globally It aims to overcome the following barriers: 1) Making existing data on hazards, vulnerabilities, and exposures more accessible and useable for FIs, with clearly communicated confidence and with analytics that does not yet exist being secured; 2) Consistency and standards to reduce fragmentation, facilitate innovative products and enable the efficient flow and use of data; 3) Assurance and suitability are needed to understand which CER analytics are best suited for particular uses and provide transparency into underlying data and methodologies, so that CER analytics can be trusted and used; 4) Unlocking innovation through supporting FIs to test new approaches in a lower-risk way; and 5) Building capability, knowledge, and skills within FIs to analyse and interpret CER data. Resolving these barriers is a necessary condition for repricing capital and avoiding its misallocation, and achieving the UK's ambitions on GF.

The vision of this CDT is to enhance society's resilience to changes in our environment through the development of Environmental Intelligence (EI): using the integration of data from multiple inter-related sources and Artificial Intelligence (AI) to provide evidence for informed decision-making, increase our understanding of environmental challenges and provide information that is required by individuals, policy-makers, institutions and businesses. Many of the most important problems we face today are related to the environment. Climate change, healthy oceans, water security, clean air, biodiversity loss, and resilience to extreme events all play a crucial role in determining our health, wealth, safety and future development. The UN's 2030 Agenda for Sustainable Development calls for a plan of action for people, planet and prosperity, aiming to take the bold and transformative steps that are urgently needed to shift the world onto a sustainable and resilient path. Developing a clear understanding of the challenges and identifying potential solutions, both for ourselves and our planet, requires high quality, accessible, timely and reliable data to support informed decision making. Beyond the quantification of the need for change and tracking developments, EI has another important role to play in facilitating change through integration of cutting edge AI technology in energy, water, transport, agricultural and other environmentally-related systems and by empowering individuals, organisations and businesses through the provision of personalized information that will support behavioural change. Students will receive training in the range of skills they will require to become leaders in EI: (i) the computational skills required to analyse data from a wide variety of sources; (ii) environmental domain-specific expertise; (iii) an understanding of governance, ethics and the potential societal impacts of collecting, mining, sharing and interpreting data, together with the ability to communicate and engage with a diverse range of stakeholders. The training programme has been designed to be applicable to students with a diverse range of backgrounds and experiences. Graduates of the CDT will be equipped with the skills they need to become tomorrow's leaders in identifying and addressing interlinked, social, economic and environmental risks. Having highly trained individuals with a wide range of expertise, together with the skills to communicate with a diverse range of stakeholders and communities, will have far reaching impact across a wide number of sectors. Traditionally, PhD students trained in the technical aspects of AI have been distinct from those trained in policy and business implementation. This CDT will break that mould by integrating students with a diverse range of backgrounds and interests and providing them with the training, in conjunction with external partners, that will ensure that they are well versed in both cutting edge methodology and on the ground policy and business implementation. The University of Exeter's expertise in inter- and trans-disciplinary environmental, climate, sustainability, circular economy and health research makes it uniquely placed to lead an inter-disciplinary CDT that will pioneer the use of AI in understanding the complex interactions between the environment, climate, natural ecosystems, human social and economic systems, and health. Students will benefit from the CDTs strong relationships with its external partners, including the Met Office. Many of these partners are employers of doctoral graduates in AI and see an increasing need for employees with skills from across multiple disciplines. Their involvement in the planning and ongoing management of the CDT will ensure that, in this rapidly changing domain, the CDT delivers leading-edge research that will enable partners and others to participate effectively in EI and lead to optimal employment opportunities for its graduates.

Flooding is the deadliest and most costly natural hazard on the planet, affecting societies across the globe. Nearly one billion people are exposed to the risk of flooding in their lifetimes and around 300 million are impacted by floods in any given year. The impacts on individuals and societies are extreme: each year there are over 6,000 fatalities and economic losses exceed US$60 billion. These problems will become much worse in the future. There is now clear consensus that climate change will, in many parts of the globe, cause substantial increases in the frequency of occurrence of extreme rainfall events, which in turn will generate increases in peak flood flows and therefore flood vast areas of land. Meanwhile, societal exposure to this hazard is compounded still further as a result of population growth and encroachment of people and key infrastructure onto floodplains. Faced with this pressing challenge, reliable tools are required to predict how flood hazard and exposure will change in the future. Existing state-of-the-art Global Flood Models (GFMs) are used to simulate the probability of flooding across the Earth, but unfortunately they are highly constrained by two fundamental limitations. First, current GFMs represent the topography and roughness of river channels and floodplains in highly simplified ways, and their relatively low resolution inadequately represents the natural connectivity between channels and floodplains. This restricts severely their ability to predict flood inundation extent and frequency, how it varies in space, and how it depends on flood magnitude. The second limitation is that current GFMs treat rivers and their floodplains essentially as 'static pipes' that remain unchanged over time. In reality, river channels evolve through processes of erosion and sedimentation, driven by the impacts of diverse environmental changes (e.g., climate and land use change, dam construction), and leading to changes in channel flow conveyance capacity and floodplain connectivity. Until GFMs are able to account for these changes they will remain fundamentally unsuitable for predicting the evolution of future flood hazard, understanding its underlying causes, or quantifying associated uncertainties. To address these issues we will develop an entirely new generation of Global Flood Models by: (i) using Big Data sets and novel methods to enhance substantially their representation of channel and floodplain morphology and roughness, thereby making GFMs more morphologically aware; (ii) including new approaches to representing the evolution of channel morphology and channel-floodplain connectivity; and (iii) combining these developments with tools for projecting changes in catchment flow and sediment supply regimes over the 21st century. These advances will enable us to deliver new understanding on how the feedbacks between climate, hydrology, and channel morphodynamics drive changes in flood conveyance and future flooding. Moreover, we will also connect our next generation GFM with innovative population models that are based on the integration of satellite, survey, cell phone and census data. We will apply the coupled model system under a range of future climate, environmental and societal change scenarios, enabling us to fully interrogate and assess the extent to which people are exposed, and dynamically respond, to evolving flood hazard and risk. Overall, the project will deliver a fundamental change in the quantification, mapping and prediction of the interactions between channel-floodplain morphology and connectivity, and flood hazard across the world's river basins. We will share models and data on open source platforms. Project outcomes will be embedded with scientists, global numerical modelling groups, policy-makers, humanitarian agencies, river basin stakeholders, communities prone to regular or extreme flooding, the general public and school children.